This paper presents a review of the turn of events and points of view of biogas in and its utilization for power, heat and in transport in the European Union (EU) and its Member States. Biogas creation has expanded in the EU, empowered by the sustainable power strategies, notwithstanding monetary, ecological and atmosphere benefits, to arrive at 18 billion m3 methane (654 PJ) in 2015, speaking to half of the worldwide biogas creation. The EU is the world chief in biogas power creation, with more than 10 GW introduced and various 17,400 biogas plants, in contrast with the worldwide biogas limit of 15 GW in 2015. In the EU, biogas conveyed 127 TJ of warmth and 61 TWh of power in 2015; about half of absolute biogas utilization in Europe was bound to warm age. Europe is the world's driving maker of biomethane for the utilization as a vehicle fuel or for infusion into the petroleum gas network, with 459 plants in 2015 creating 1.2 billion m3 and 340 plants taking care of into the gas network, with a limit of 1.5 million m3. Around 697 biomethane filling stations guaranteed the utilization 160 million m3 of biomethane as a transport fuel in 2015.

https://www.longdom.org/open-access/biogas-developments-and-perspectives-in-europe.pdf

Biogas Developments and Perspectives in Europe

A technique is described that measures the instantaneous three-dimensional temperature distribution in water using two-color laser-induced fluorescence (LIF). Two fluorescent dyes, Rhodamine B and Rhodamine 110, are used as temperature indicators. A laser light sheet scanned across the entire measurement volume excites the fluorescent dye, and an optical system involving a color beam splitter gives the intensity distribution of the individual fluorescent dyes on two separate monochrome CCD cameras. The ratio of these fluorescence intensities at each point of the image is calibrated against the temperature to eliminate the effect of the fluctuation of illuminating light intensity. A stable thermally stratified layer was measured by this system to evaluate the total accuracy of the measurement system. The random error of the measurement was ±1.4 K with 95% confidence. Measurements of thermal convection over a heated horizontal surface show temperature iso-surfaces having typical structures such as plumes, ridges and thermals.

Whole Field Measurement of Temperature in Water Using Two-Color Laser Induced Fluorescence

Principles of non-intrusive diagnostic techniques and their applications for fundamental studies of combustion instabilities in gas turbine combustors: A brief review

CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers

Online in situ prediction of 3-D flame evolution has been long desired and is considered to be the Holy Grail for the combustion community. Recent advances in computational power have facilitated the development of computational fluid dynamics (CFD), which can be used to predict flame behaviours. However, the most advanced CFD techniques are still incapable of realizing online in situ prediction of practical flames due to the enormous computational costs involved. In this work, we aim to combine the state-of-the-art experimental technique (that is, time-resolved volumetric tomography) with deep learning algorithms for rapid prediction of 3-D flame evolution. Proof-of-concept experiments conducted suggest that the evolution of both a laminar diffusion flame and a typical non-premixed turbulent swirl-stabilized flame can be predicted faithfully in a time scale on the order of milliseconds, which can be further reduced by simply using a few more GPUs. We believe this is the first time that online in situ prediction of 3-D flame evolution has become feasible, and we expect this method to be extremely useful, as for most application scenarios the online in situ prediction of even the large-scale flame features are already useful for an effective flame control.

Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning

Nonlinear tomographic absorption spectroscopy (NTAS) is an emerging gas sensing technique for reactive flows that has been proven to be capable of simultaneously imaging temperature and concentration of absorbing gas. However, the nonlinear tomographic problems are typically solved with an optimization algorithm such as simulated annealing which suffers from high computational cost. This problem becomes more severe when thousands of tomographic data needs to be processed for the temporal resolution of turbulent flames. To overcome this limitation, in this work we propose a reconstruction method based on convolutional neural networks (CNN) which can take full advantage of the large amount tomographic data to build an efficient neural networks to rapidly predict the reconstruction by feeding the sinograms to it. Simulative studies were performed to investigate how the parameters will affect the performance of neural networks. The results show that CNN can effectively reduce the computational cost and at the same time achieve a similar accuracy level as SA. The successful demonstration CNN in this work indicates possible applications of other sophisticated deep neural networks such as deep belief networks (DBN) and generative adversarial networks (GAN) to nonlinear tomography. © 2018 Elsevier Ltd.

Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning

kHz-rate volumetric flame imaging using a single camera

Three-dimensional computed tomography of chemiluminescence (CTC) for combustion diagnostics is attracting a surged research interest due to recent progress in sensor technologies and reduced costs of high-speed cameras. For example, it has been applied to recover the 3D distributions of intermediate chemical species such as CH* and OH*, heat release rate, and flame topology. Although these applications were demonstrated to be successful, there are still a few drawbacks of this technique that have not be cured. For example, to the best of the authors' knowledge, all the imaging models that have been developed so far ignore the imperfections of cameras such as lens distortion and skewness. However, this will unavoidably introduce errors into the weight matrix. In addition, spatial resolution of a CTC system is a critical performance parameter. However, it has only been studied qualitatively and no quantitative quantification method is reported so far. This work aims to solve these problems by improving the imaging model and developing a method based on edge spread function for the quantification of spatial resolution. Although this work is conducted under the context of CTC for combustion diagnostics, it also provides useful insights for other tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence.

On the quantification of spatial resolution for three-dimensional computed tomography of chemiluminescence.

Computed tomography of a chemiluminescence (CTC) system was implemented to provide time-resolved 3D measurements of an unconfined turbulent swirl flame This system was designed in a cost-effective manner and employed three customized view registration assemblies to simultaneously capture eight projections of the target flame at a repetition rate of 4 kHz Both time-resolved and time-averaged tomographic reconstructions were performed based on data acquired for a duration of 250 ms Both qualitative and quantitative validations suggested the correctness of our implementation The time-resolved instantaneous reconstructions successfully captured the evolution of the structural features of the swirl flame such as local extinctions and the helical mode Based on the reconstructions, the centroids of chemiluminescence for all the layers were calculated The trajectory of these centroids provided insights into the flow motion and suggested a rotating helical structure of the swirl flame These results demonstrated the feasibility of resolving the dynamics of turbulent swirl flames with a kHz temporal resolution using the relatively inexpensive CTC system

Hecong Liu

Papers

Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning

Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning

kHz-rate volumetric flame imaging using a single camera

On the quantification of spatial resolution for three-dimensional computed tomography of chemiluminescence.

Time-resolved measurements of a swirl flame at 4 kHz via computed tomography of chemiluminescence.